






import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


A_						:						Any						      =					re.compile(r'\s+')

def     snake_case   (UpperCAmelCase__    )			->							List[str]:
  return {"hash": hashlib.mda(re.sub(UpperCAmelCase__      ,       ''      ,       example['content']    ).encode('utf-8'    )    ).hexdigest()}

def     snake_case   (UpperCAmelCase__    )			->							str:
  UpperCamelCase_:   Optional[int]        =							[len(UpperCAmelCase__    ) for line in example['content'].splitlines()]
  return {"line_mean": np.mean(UpperCAmelCase__    ), "line_max": max(UpperCAmelCase__    )}

def     snake_case   (UpperCAmelCase__    )			->							List[str]:
  UpperCamelCase_:   Any        =							np.mean([c.isalnum() for c in example['content']]    )
  return {"alpha_frac": alpha_frac}

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							List[Any]:
  if example["hash"] in uniques:
    uniques.remove(example['hash']    )
    return True
  else:
    return False

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__=5    )			->							Union[str, Any]:
  UpperCamelCase_:   Optional[Any]        =							['auto-generated', 'autogenerated', 'automatically generated']
  UpperCamelCase_:   Optional[Any]        =							example['content'].splitlines()
  for _, line in zip(range(UpperCAmelCase__    )      ,       UpperCAmelCase__    ):
    for keyword in keywords:
      if keyword in line.lower():
        return {"autogenerated": True}
  else:
    return {"autogenerated": False}

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__=5      ,       UpperCAmelCase__=0.05    )			->							List[str]:
  UpperCamelCase_:   List[str]        =							['unit tests', 'test file', 'configuration file']
  UpperCamelCase_:   Dict        =							example['content'].splitlines()
  UpperCamelCase_:   Optional[int]        =							0
  UpperCamelCase_:   Optional[Any]        =							0
  # first test
  for _, line in zip(range(UpperCAmelCase__    )      ,       UpperCAmelCase__    ):
    for keyword in keywords:
      if keyword in line.lower():
        return {"config_or_test": True}
    # second test
  UpperCamelCase_:   Union[str, Any]        =							example['content'].count('\n'    )
  UpperCamelCase_:   List[Any]        =							int(coeff * nlines    )
  for line in lines:
    count_config += line.lower().count('config'    )
    count_test += line.lower().count('test'    )
    if count_config > threshold or count_test > threshold:
      return {"config_or_test": True}
  return {"config_or_test": False}

def     snake_case   (UpperCAmelCase__    )			->							Any:
  UpperCamelCase_:   List[str]        =							['def ', 'class ', 'for ', 'while ']
  UpperCamelCase_:   int        =							example['content'].splitlines()
  for line in lines:
    for keyword in keywords:
      if keyword in line.lower():
        return {"has_no_keywords": False}
  return {"has_no_keywords": True}

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__=4    )			->							int:
  UpperCamelCase_:   str        =							example['content'].splitlines()
  UpperCamelCase_:   Optional[Any]        =							0
  for line in lines:
    counter += line.lower().count('='    )
    if counter > minimum:
      return {"has_few_assignments": False}
  return {"has_few_assignments": True}

def     snake_case   (UpperCAmelCase__    )			->							str:
  UpperCamelCase_:   int        =							tokenizer(example['content']      ,       truncation=UpperCAmelCase__    )['input_ids']
  UpperCamelCase_:   Optional[Any]        =							len(example['content']    ) / len(UpperCAmelCase__    )
  return {"ratio": ratio}

def     snake_case   (UpperCAmelCase__    )			->							Dict:
  UpperCamelCase_:   Optional[Any]        =							{}
  results.update(get_hash(UpperCAmelCase__    )    )
  results.update(line_stats(UpperCAmelCase__    )    )
  results.update(alpha_stats(UpperCAmelCase__    )    )
  results.update(char_token_ratio(UpperCAmelCase__    )    )
  results.update(is_autogenerated(UpperCAmelCase__    )    )
  results.update(is_config_or_test(UpperCAmelCase__    )    )
  results.update(has_no_keywords(UpperCAmelCase__    )    )
  results.update(has_few_assignments(UpperCAmelCase__    )    )
  return results

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							int:
  if not check_uniques(UpperCAmelCase__      ,       UpperCAmelCase__    ):
    return False
  elif example["autogenerated"]:
    return False
  elif example["line_max"] > args.line_max:
    return False
  elif example["line_mean"] > args.line_mean:
    return False
  elif example["alpha_frac"] < args.alpha_frac:
    return False
  elif example["ratio"] < args.min_token_ratio:
    return False
  elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
    return False
  elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
    return False
  elif example["has_few_assignments"]:
    return False
  else:
    return True

def     snake_case   (UpperCAmelCase__    )			->							str:
  with open(UpperCAmelCase__      ,       'rb'    ) as f_in:
    with gzip.open(str(UpperCAmelCase__    ) + '.gz'      ,       'wb'      ,       compresslevel=6    ) as f_out:
      shutil.copyfileobj(UpperCAmelCase__      ,       UpperCAmelCase__    )
  os.unlink(UpperCAmelCase__    )


# Settings
A_						:						Tuple						      =					HfArgumentParser(PreprocessingArguments)
A_						:						str						      =					parser.parse_args()
if args.num_workers is None:
   A_						:						Optional[int]						      =					multiprocessing.cpu_count()
A_						:						Dict						      =					AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
A_						:						Any						      =					time.time()
A_						:						Tuple						      =					load_dataset(args.dataset_name, split='train')
print(F'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
A_						:						List[Any]						      =					time.time()
A_						:						Union[str, Any]						      =					ds.map(preprocess, num_proc=args.num_workers)
print(F'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
A_						:						Optional[int]						      =					set(ds.unique('hash'))
A_						:						Union[str, Any]						      =					len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
A_						:						Optional[int]						      =					time.time()
A_						:						Optional[int]						      =					ds.filter(filter, fn_kwargs={'uniques': uniques, 'args': args})
print(F'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(F'''Size of filtered dataset: {len(ds_filter)}''')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
   A_						:						str						      =					time.time()
   A_      ,					A_						:						Optional[Any]						      =					deduplicate_dataset(ds_filter, args.jaccard_threshold)
   print(F'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
   print(F'''Size of deduplicate dataset: {len(ds_filter)}''')

# Save data in batches of samples_per_file
A_						:						Any						      =					Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
   with open(output_dir / 'duplicate_clusters.json', 'w') as f:
      json.dump(duplicate_clusters, f)

A_						:						Optional[int]						      =					output_dir / 'data'
data_dir.mkdir(exist_ok=True)

A_						:						Tuple						      =					time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
   A_						:						Dict						      =					str(data_dir / F'''file-{file_number+1:012}.json''')
   A_						:						List[str]						      =					min(len(ds_filter), index + args.samples_per_file)
   ds_filter.select(list(range(index, end_index))).to_json(file_path)
   compress_file(file_path)
print(F'''Time to save dataset: {time.time()-t_start:.2f}''')
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase					):


			"""simple docstring"""






			@register_to_config
			def __init__(  self ,  *,
			    lowerCAmelCase__ = 4 ,  lowerCAmelCase__ = 7_68 ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							super().__init__()

							__lowercase					     =				nn.Parameter(torch.zeros(lowerCAmelCase__		)		)

							# parameters for additional clip time embeddings
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)

							# parameters for encoder hidden states
							__lowercase					     =				clip_extra_context_tokens
							__lowercase					     =				nn.Linear(
							    lowerCAmelCase__ ,  self.clip_extra_context_tokens * cross_attention_dim		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.LayerNorm(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *, lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__lowercase					     =				image_embeddings.shape[0]
											__lowercase					     =				self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
											__lowercase					     =				classifier_free_guidance_embeddings.expand(
											    lowerCAmelCase__ ,  -1		)
											__lowercase					     =				torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,  dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase					     =				prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase					     =				self.embedding_proj(lowerCAmelCase__		)
							__lowercase					     =				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__		)
							__lowercase					     =				time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase					     =				self.clip_extra_context_tokens_proj(lowerCAmelCase__		)
							__lowercase					     =				clip_extra_context_tokens.reshape(lowerCAmelCase__ ,  -1 ,  self.clip_extra_context_tokens		)
							__lowercase					     =				clip_extra_context_tokens.permute(0 ,  2 ,  1		)

							__lowercase					     =				self.encoder_hidden_states_proj(lowerCAmelCase__		)
							__lowercase					     =				self.text_encoder_hidden_states_norm(lowerCAmelCase__		)
							__lowercase					     =				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,  dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings